The present invention relates generally to solutions for enhancing the information contents of medical image data. More particularly the invention relates to an image processing system according to the preamble of claim 1 and a corresponding method. The invention also relates to a computer program and a processor-readable medium.
The process of defining which voxels that represent a particular anatomic structure, or so-called organ delineation, is one of the most tedious and time-consuming parts of radiotherapy planning. This process usually involves manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional data set of high resolution used for planning.
Pekar, V., et al., “Automated Model-Based Organ Delineation for Radiotherapy Planning in Prostatic Region”, International Journal of Radiation Oncology—Biology—Physics, Vol. 60, No. 3, pp 973-980, 2004 discloses a method for adapting 3D deformable surface models to the boundaries of anatomic structures of interest. The adaptation is based on a tradeoff between deformations of the model induced by its attraction to certain image features and the shape integrity of the model. Problematic areas where the automated model adaptation may fail can be corrected via interactive tools.
US 2011/0268330 describes systems and methods for contouring a set of medical images. An example system may include an image database, an image deformation engine and a contour transformation engine. The image database may be used to store a set of medical images. The image deformation engine may be configured to receive a source image and a target image from the set of medical images in the image database, and may be further configured to use a deformation algorithm with the source image and the target image to generate deformation field data that is indicative of changes between one or more objects from the source image to the target image. The contour transformation engine may be configured to receive source contour data that identifies the one or more objects within the source image, and be further configured to use the deformation field data and the source contour data to generate automatic target contour data that identifies the one or more objects within the target image. The image deformation engine and the contour transformation engine may comprise software instructions stored in one or more memory devices and be executable by one or more processors.
Wimmer, A., et al, “Two-stage Semi-automatic Organ Segmentation Framework using Radial Basis Functions and Level Sets”, 10th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2007, 3D Segmentation in The Clinic: Grand Challenge, 29 Oct. 2007 (2007-10-29), pages 179-188 discloses a two-stage semi-automatic algorithm that is able to segment complex structures like the liver shape with moderate user interaction. A first stage of the algorithm involves manual delineation of cross-sections of the anatomical structure in 2D multi-planar reconstruction views. From this set of contours, an initial 3D surface is reconstructed using radial basis functions. In a second step, the surface is evolved using a level set algorithm incorporating a new combination of both image information and shape information, the latter being derived from the initial contours.